Ejemplo n.º 1
0
 def test_one_dee_distributions(self):
     p_1 = {(1, ) : 0.1,
            (2, ) : 0.2,
            (3, ) : 0.3,
            (4, ) : 0.4,}
     
     f = lambda state : (0, )
     p = statistics.map_distribution(f, p_1)
     assert len(p) == 1
     assert ((0, ) in p) and (p[(0, )] == 1.0)
     
     mu = statistics.expectation(p_1)
     mu_goal = numpy.array([3.0])
     assert_almost_equal(mu, mu_goal)
     
     sigma_squared = statistics.variance(p_1)
     sigma_squared_goal = numpy.asarray([1.0])
     assert_almost_equal(sigma_squared, sigma_squared_goal)
Ejemplo n.º 2
0
    def test_one_dee_distributions(self):
        p_1 = {
            (1, ): 0.1,
            (2, ): 0.2,
            (3, ): 0.3,
            (4, ): 0.4,
        }

        f = lambda state: (0, )
        p = statistics.map_distribution(f, p_1)
        assert len(p) == 1
        assert ((0, ) in p) and (p[(0, )] == 1.0)

        mu = statistics.expectation(p_1)
        mu_goal = numpy.array([3.0])
        assert_almost_equal(mu, mu_goal)

        sigma_squared = statistics.variance(p_1)
        sigma_squared_goal = numpy.asarray([1.0])
        assert_almost_equal(sigma_squared, sigma_squared_goal)
Ejemplo n.º 3
0
 def test_two_dee_distributions(self):
     p_2 = {(0, 0) : 0.2,
            (0, 1) : 0.3,
            (1, 0) : 0.2,
            (3, 3) : 0.3,}
     
     f = lambda state : (state[0], )
     p = statistics.map_distribution(f, p_2)
     assert len(p) == 3
     assert ((0, ) in p) and (p[(0, )] == 0.5)
     assert ((1, ) in p) and (p[(1, )] == 0.2)
     assert ((3, ) in p) and (p[(3, )] == 0.3)
     
     mu = statistics.expectation(p_2)
     mu_goal = numpy.asarray([1.1, 1.2])
     assert_almost_equal(mu, mu_goal)
     
     cov = statistics.covariance(p_2)
     cov_goal = -1.1*-1.2*0.2 -1.1*-0.2*0.3 -0.1*-1.2*0.2 +1.9*1.8*0.3
     assert_almost_equal(cov, cov_goal)
Ejemplo n.º 4
0
    def test_two_dee_distributions(self):
        p_2 = {
            (0, 0): 0.2,
            (0, 1): 0.3,
            (1, 0): 0.2,
            (3, 3): 0.3,
        }

        f = lambda state: (state[0], )
        p = statistics.map_distribution(f, p_2)
        assert len(p) == 3
        assert ((0, ) in p) and (p[(0, )] == 0.5)
        assert ((1, ) in p) and (p[(1, )] == 0.2)
        assert ((3, ) in p) and (p[(3, )] == 0.3)

        mu = statistics.expectation(p_2)
        mu_goal = numpy.asarray([1.1, 1.2])
        assert_almost_equal(mu, mu_goal)

        cov = statistics.covariance(p_2)
        cov_goal = -1.1 * -1.2 * 0.2 - 1.1 * -0.2 * 0.3 - 0.1 * -1.2 * 0.2 + 1.9 * 1.8 * 0.3
        assert_almost_equal(cov, cov_goal)